UCL Machine Reading Group: Four Factor Framework For Fact Finding (HexaF)
Takuma Yoneda, Jeff Mitchell, Johannes Welbl, Pontus Stenetorp, Sebastian Riedel · 2018
In this paper we describe our 2 nd place FEVER shared-task system that achieved a FEVER score of 62.52% on the provisional test set (without additional human evaluation), and 65.41% on the development set.Our system is a four stage model consisting of document retrieval, sentence retrieval, natural language inference and aggregation.Retrieval is performed leveraging task-specific features, and then a natural language inference model takes each of the retrieved sentences paired with the claimed fact.The resulting predictions are aggregated across retrieved sentences with a Multi-Layer Perceptron, and re-ranked corresponding to the final prediction.